- Taschenbuch: 280 Seiten
- Verlag: Packt Publishing (25. November 2013)
- Sprache: Englisch
- ISBN-10: 1782163360
- ISBN-13: 978-1782163367
- Größe und/oder Gewicht: 19 x 1,6 x 23,5 cm
- Durchschnittliche Kundenbewertung: 1 Kundenrezension
- Amazon Bestseller-Rang: Nr. 170.834 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Python Data Visualization Cookbook (Englisch) Taschenbuch – 25. November 2013
|Neu ab||Gebraucht ab|
Wird oft zusammen gekauft
Kunden, die diesen Artikel gekauft haben, kauften auch
Es wird kein Kindle Gerät benötigt. Laden Sie eine der kostenlosen Kindle Apps herunter und beginnen Sie, Kindle-Bücher auf Ihrem Smartphone, Tablet und Computer zu lesen.
Geben Sie Ihre Mobiltelefonnummer ein, um die kostenfreie App zu beziehen.
Wenn Sie dieses Produkt verkaufen, möchten Sie über Seller Support Updates vorschlagen?
Über den Autor und weitere Mitwirkende
Igor Milovanovic is an experienced developer with a strong background in Linux system knowledge and software engineering. He is skilled at building scalable, data-driven, distributed-software-rich systems.
He is an Evangelist for high-quality systems design who holds strong interests in software architecture and development methodologies. He is always persistent on advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration.
He also possesses a solid knowledge of product development. Having field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa.
Welche anderen Artikel kaufen Kunden, nachdem sie diesen Artikel angesehen haben?
I tried to find not only pros and found three missing points (at least for me).
* most of the examples in the book NumPy & Matplotlib, I was quite surprised that I did not see PANDAS library being mentioned which becomes a standard for handling data in Python nowadays.
* there's one recipe on how to access a typical sql database from python and extract data using sql. I missed another example for recently popular nosql databases like MongoDB.
* I missed a chapter on d3py. D3PY is a Python frontend for D3.js - Data Driven documents for the web.
Still I would like to recommend reeading this book. It is beneficial for intermediate+ Python developers, students or scientists that already know Python basics and is a good collection of recipes. If one needs to dive in quickly to present data on a chart, then this book is for you. For absolute beginners a Python introduction is a must.
Die hilfreichsten Kundenrezensionen auf Amazon.com
Author uses lots of examples to demonstrate different visualization terminology, which really helps people to understand the abstract image processing technology. This book also shows you how to setup the virtual env to isolate development environment. Although the main purpose of this book is to teach how to visualize data, many of the example programs also show the best python development practice. Majority of the code is runnable without touch-up. Some typos are pretty easy to be spotted. I would recommend it to people who already have python experience and would like to extend their experience to data visualization area.
While this is not a rigorous tutorial, the author goes into exactly the right depth to allow you to make a decision on methodology and begin implementing right away.
If, rather than becoming a NumPy scholar, you expect to have to deliver results from varied species of data, having this in your back pocket will help you accomplish that.
There no any comments about Python as a language, so be sure that you know it quite good. No need to be a senior developer, but strong junior, would be nice.
Ähnliche Artikel finden